10 citations · 29 across the 6 of their papers we have counts for
6 papers
Learning Not to Spoof
David Byrd
As intelligent trading agents based on reinforcement learning (RL) gain prevalence, it becomes more important to ensure that RL agents obey laws, regulations, and human behavioral…
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou +1
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted…
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
David Byrd, Antigoni Polychroniadou
Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within…
The Importance of Low Latency to Order Book Imbalance Trading Strategies
David Byrd, Sruthi Palaparthi, Maria Hybinette +1
There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies. This belief is evidenced by t…
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Svitlana Vyetrenko, David Byrd, Nick Petosa +4
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing. Furthermore, simulation is important…
Explaining Agent-Based Financial Market Simulation
David Byrd
This paper is intended to explain, in simple terms, some of the mechanisms and agents common to multiagent financial market simulations. We first discuss the necessity to include a…